Targeted Delivery of Amikacin into Granuloma
Bibliographic record
Abstract
RATIONALE: Nontuberculous mycobacterial (NTM) infection is a growing problem in the United States and remains underrecognized in the developing world. The management of NTM infections is further complicated by several factors, including the need to use high systemic doses of toxic agents, the length of therapy, and the development of drug resistance. OBJECTIVES: We have evaluated the use of monocyte-derived dendritic cells (DCs) as a delivery vehicle for a luminescent derivative of amikacin prepared by conjugation to fluorescein isothiocyanate (FITC) (amikacin-FITC) into granulomas formed in the tissues of mice infected with Mycobacterium avium. METHODS: Amikacin-FITC was prepared and quantitative fluorescence was used to track the intracellular uptake of this modified antibiotic. The antibiotic activity of amikacin-FITC was also determined to be comparable to unmodified amikacin against M. avium. Amikacin-FITC-loaded DCs were first primed with M. avium, and then the cells were injected into the tail vein of infected mice. After 24 hours, the mice were sacrificed and the tissues were analyzed under fluorescence microscope. MEASUREMENTS AND MAIN RESULTS: We found that we were able to deliver amikacin into granulomas in a mouse model of disseminated mycobacterial infection. No increase in levels of monocyte chemoattractant protein-1 and its CCR2 as markers of inflammation were found when DCs were treated with amikacin-FITC. CONCLUSIONS: DC-based drug delivery may be an adjunct and useful method of delivering high local concentrations of antibiotics into mycobacterial granulomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".